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Studying the breakdown characteristics of nano-modified transformer oil at low temperature is helpful to improve the breakdown voltage of transformer oil at low temperature. The breakdown characteristics of oil samples under different voltage forms, different temperatures and different water contents at-30 ∼30 °C were studied by using a relatively perfect test system. The results show that in the test temperature range, the breakdown voltage of the oil sample decreases first and then increases, and there is a minimum value. Under the three moisture contents of 26 ppm, 61 ppm and 85 ppm, the breakdown voltage of pure oil and nano-oil is the lowest when the moisture content is 85 ppm. The addition of nano-materials increases the breakdown voltage of oil samples, and the increase is small near zero degrees Celsius. When the water content is 26 ppm, the average increase rate is 8.57 %. The average increase rate is 14.35 %. The largest increase is above zero, with an average increase rate of 19.6 %. Nanomaterials reduce the dispersion of breakdown voltage of transformer oil. Compared with pure oil, the dispersion reduction rate is about 1.03.

To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm–Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.

Based on the limitation of the characteristics of materials, in the transmission and distribution network of power system, the relationship between cable current carrying capacity and temperature is inseparable. Improving the ampacity of power cables within the allowable range of temperature is important to the power grid. Using COMSOL multi physical field simulation software, the model of cable to calculate the current carrying capacity is established, and the relationship between cable capacity and temperature field is analyzed. On this premise, the effects of cable placement, convective thermal conductivity and different environmental conditions on the temperature field distribution are further analyzed. The simulation results show that increasing the cable spacing, improving the convective thermal conductivity and maintaining a low ambient temperature the current carrying capacity of the cable can be improved.

It is very important for the safe and stable operation of power grid to detect the location and scope of mined-out area under transmission line accurately and efficiently by comprehensive geophysical method. According to the geophysical properties of goaf, resistivity method, electromagnetic method and seismic method are often used to detect goaf, but due to the actual interference and geophysical multi-solution, a single geophysical exploration method is often difficult to achieve ideal results. In this paper, the symmetrical quadrupole section resistivity method and shallow seismic reflection method are used to detect the mined-out area under the high-voltage overhead line. The study shows that the resistivity method and seismic method are mutually validated and complementary, which effectively reduces the multi-resolution of single geophysical method interpretation, and improves the detection resolution and the reliability of interpretation results. It provides an effective basis for delineating goaf.

In this paper, we obtain some important inequalities of Hessian quotient operators, and global C2 estimates of the Neumann problem of Hessian quotient equations. By the method of continuity, we establish the existence theorem of k-admissible solutions of the Neumann problem of Hessian quotient equations.